For many years microorganisms have been exploited in industrial applications for the production of valuable commercial products, such as industrial enzymes, hormones and antibodies. Despite the fact that recombinant DNA technology has been used in an attempt to increase the productivity of these microorganisms, the use of metabolic genetic engineering to improve strain performance, particularly in industrial fermentations has been disappointing.
A common strategy used to increase microbial strain performance is to alter gene expression, and a number of means have been used to achieve this end. One approach includes the cloning of a heterologous or a homologous gene in a multi-copy plasmid in a selected host strain. Another approach concerns altering chromosomal gene expression. This has been accomplished by various methods some of which include: (1) site-specific mutations, deletions or insertions at a predetermined region of a chromosome; (2) reliance on transposons to insert DNA randomly into chromosomes and (3) altering of native regulatory regions of a gene at its chromosomal location. The alteration of regulatory regions can be accomplished for example, by changing promoter strength or by using regulatable promoters which are influenced by inducer concentration. Reference is made to Jensen and Hammer, (1998) Biotechnology and Bioengineering 58:193-195; Jensen and Hammer (1998) Appl. Environ. Microbio. 64:82-85; and Khlebnikov et al. (2001) Microbiol. 147:3241. Other techniques used to replace regulatory regions of chromosomal gene have been disclosed in Abdel-Hamid et al. (2001) Microbiol. 147:1483-1498 and Repoila and Gottesman (2001) J. Bacteriol. 183:4012-4023.
With respect to optimizing metabolic pathway engineering in a selected host, the above-mentioned approaches have had limited success and each approach has certain disadvantages. Research has shown the expression level of a genetically modified gene on a plasmid is not necessarily correlated with the level of expression of the same modified gene located in the chromosome (See Khlebnikov et al. (2001) Microbiol. 147:3241 and McCraken and Timms (1999) J. Bacteriol. 18:6569).
Moreover, the effect of increasing expression of one gene in a metabolic pathway may only have a marginal effect on the flux through that metabolic pathway. This may be true even if the gene being manipulated codes for an enzyme in a rate-limiting step because control of a metabolic pathway may be distributed over a number of enzymes. Therefore, while a gene has been engineered to achieve a high level of expression, for example a 10 to 100 fold increase in expression, the overall performance of the engineered microorganism in a bioreactor may decrease. The decrease could be due to the balance of other factors involved in the metabolic pathway or the depletion of other substances necessary for optimum cell growth.
The above problem is addressed in part by Jensen and Hammer (WO 98/07846). The disclosure of WO98/07846 describes the construction of a set of constitutive promoters that provide different levels of gene expression. Specifically, artificial promoter libraries are constructed comprising variants of a regulatory region that includes a −35 consensus box, a −10 consensus box and a spacer (linker) region that lies between these consensus regions. However, one of the drawbacks of the method described in WO 98/07846 is the extensive screening (in terms of time and numbers of steps), which would be required to create a library of clones with different levels of gene expression. It is also disclosed in the reference that the modulation of promoter strength, by a few base-pair changes in the consensus sequences or by changes in the linker sequence, would result in a large impact in promoter strength, and therefore, it would not be feasible to achieve small steps on promoter strength modulation.
Therefore, a need still exists in the area of metabolic pathway engineering to develop a quick and efficient means of determining the optimum expression of a gene of interest in a metabolic pathway which in turn results in an optimization of strain performance for a desired product. The present method satisfies this need by providing a method to characterize small changes in gene expression level and hence allowing for the selection of a cell providing an optimum level of expression.